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<p>The Multi-Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) is a new community modeling infrastructure that enables the study of atmospheric composition and chemistry across all relevant scales. We develop a MUSICAv0 grid with Africa refinement (<span class="inline-formula">∼</span> 28 <span class="inline-formula">km</span> <span class="inline-formula">×</span> 28 <span class="inline-formula">km</span> over Africa). We evaluate the MUSICAv0 simulation for 2017 with in situ observations and compare the model results to satellite products over Africa. A simulation from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), a regional model that is widely used in Africa studies, is also included in the analyses as a reference. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Both models underestimate carbon monoxide (CO) compared to in situ observations and satellite <span class="inline-formula">CO</span> column retrievals from the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument. MUSICAv0 tends to overestimate ozone (<span class="inline-formula">O₃</span>), likely due to overestimated stratosphere-to-troposphere flux of ozone. Both models significantly underestimate fine particulate matter (<span class="inline-formula">PM_2.5</span>) at two surface sites in East Africa. The MUSICAv0 simulation agrees better with aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and tropospheric nitrogen dioxide (<span class="inline-formula">NO₂</span>) column retrievals from the Ozone Monitoring Instrument (OMI) than WRF-Chem. MUSICAv0 has a consistently lower tropospheric formaldehyde (<span class="inline-formula">HCHO</span>) column than OMI retrievals. Based on model–satellite discrepancies between MUSICAv0 and WRF-Chem and MOPITT <span class="inline-formula">CO</span>, MODIS AOD, and OMI tropospheric <span class="inline-formula">NO₂</span>, we find that future field campaign(s) and more in situ observations in the East African region (5<span class="inline-formula">^∘</span> S–5<span class="inline-formula">^∘</span> N, 30–45<span class="inline-formula">^∘</span> E) could substantially improve the predictive skill of atmospheric chemistry model(s). This suggested focus region exhibits the largest model–in situ observation discrepancies, as well as targets for high population density, land cover variability, and anthropogenic pollution sources.</p>